Disclosure : I wrote the original ideas and examples myself, and used AI to help polish, shorten, and translate this post into English. If you prefer not to read AI-assisted writing, feel free to skip it. I’ve been working on AI implementation projects for companies, and recently ran into a question that I think is going to become much more important as AI agents move into actual workflows. One client is a B2B company. Traditionally, a salesperson spends a lot of time finding prospects, researching companies, identifying contacts, drafting outreach emails, preparing for meetings, taking notes, updating the CRM, and planning follow-ups. Once we broke the workflow down, a large percentage of that work looked automatable. A lead agent can find prospects. A research agent can enrich company and contact information. An outreach agent can draft emails. A meeting agent can summarize calls. A CRM agent can update opportunities and suggest the next action. Our estimate was that repetitive manual work could potentially fall to around 30-40% of the previous level. Then management asked a very reasonable question: From the company’s perspective, this makes sense. You invest in AI because you expect higher productivity. But from the employee’s perspective, another question appears immediately: I think this is where AI adoption stops being primarily a technology problem and becomes an organizational design problem. Many traditional KPIs assume that actions are expensive A lot of performance metrics were designed around a simple constraint: human time is limited. So companies measure things like calls made, emails sent, tickets resolved, designs produced, contracts reviewed, features shipped, etc. Those metrics made more sense when each action consumed meaningful human effort. AI changes the economics of those actions. Generating 300 outbound emails is cheap. That doesn’t mean 300 emails create more value than 50 well-targeted ones. Generating 100 designs is cheap. That doesn’t mean 100 designs deserve to go into production. Coding agents can produce huge amounts of code. Lines of code still tell us very little about maintainability, reliability, architecture, or actual business value. When the cost of an action approaches zero, the number of actions becomes a weaker proxy for value. Simply increasing the KPI from 30 to 100 may create more activity without creating proportionally more revenue, profit, customer satisfaction, or quality. AI also changes what a “job” actually consists of I’m increasingly finding it useful to split a job into two categories. One category includes work that is relatively easy to delegate to agents: repetitive tasks, structured inputs and outputs, clear rules, historical examples, verifiable results, and manageable error costs. The other category includes things like defining goals, handling ambiguity, making tradeoffs, managing exceptions, making high-risk decisions, dealing with important customers, coordinating people, and taking responsibility for outcomes. As more execution gets delegated, the human workflow starts to look something like: Human sets objectives and constraints → agents research/generate/execute/monitor → human handles exceptions and important decisions → system records results → human improves the rules and workflow. That means a knowledge worker may gradually become a manager of digital labor. A salesperson might manage several agents for prospecting, research, outreach, meetings, and CRM. A developer might work with coding, testing, review, documentation, and research agents. An operations person might supervise content, analytics, monitoring, and execution agents. So professional competence may increasingly include questions like: Can you tell when the agent is wrong? Do you know when its output can be trusted? Can you intervene effectively when something breaks? Can you improve the system so it performs better next time? This creates a problem with how we value employees Imagine two senior customer support employees. Employee A is extremely good and personally resolves 150 difficult cases per day. But most of their knowledge stays in their head. Employee B resolves only 100 cases personally, but documents recurring problems, improves the knowledge base, labels agent failures, creates better rules, and improves the support agent’s accuracy by 5% across a 30-person team. Traditional performance systems will often favor Employee A because their individual output is higher. But Employee B may have created much more organizational value. I think companies will increasingly need to distinguish between: Direct output: What did this employee personally produce? Leveraged output: How much did this employee improve the productivity of agents, systems, and other employees? AI makes leveraged output much more important because knowledge can now be operationalized. A good rule isn’t just a document anymore. It can be executed thousands of times by an agent. A strong employee’s judgment can become infrastructure. This also explains why strong employees may resist “training the AI” Suppose your best employee gives the company their playbook. The company turns it into rules, prompts, workflows, and agents. Junior employees become more capable. The team becomes more efficient. The company then gives the expert more work. But their compensation, promotion opportunities, and status remain unchanged. Why would they keep contributing their know-how? Companies often interpret reluctance to document processes, maintain knowledge bases, or label AI failures as “resistance to change.” Sometimes the incentive system is simply telling employees that keeping knowledge private is safer for them personally. If companies want employees to continuously improve AI systems, they may eventually need to recognize system contribution as part of performance. For example: Did this employee improve agent accuracy? Did they identify an important failure mode? Did they create a reusable rule or workflow? Did they reduce human intervention? Did their work improve the productivity of an entire team? Of course, this can easily become another bad KPI system. Measuring “number of prompts written” or “number of knowledge-base articles created” would probably just recreate the same problem. The metric has to connect to actual effects. One rule that reduces an agent’s error rate by 10% can be worth far more than 100 unused documentation pages. The hardest question may be how to distribute the productivity gains Suppose AI saves a team 1,000 hours per year. The company should obviously capture some of that value. Higher capacity, lower unit costs, and better margins are legitimate returns on the investment. But if every saved hour is immediately converted into more of the same work, employees quickly learn one lesson: Using AI well means I get more work. That is probably not a stable incentive structure. A healthier model might distribute the productivity gains across three areas: The company captures economic benefits. Some released time is reinvested into higher-value work, such as customer relationships, innovation, complex problems, or new opportunities. Employees receive some benefit through compensation, promotion, better roles, reduced repetitive work, or explicit recognition for system-level contributions. Then the loop becomes: Employee contributes knowledge → agents improve → team productivity increases → company benefits → employee benefits → employee has a reason to keep improving the system. This is why I think AI adoption will eventually force companies to redesign performance management. Revenue, profit, retention, quality, and customer outcomes will still matter. But companies may need to add another dimension: How much leverage does this person create through other humans and AI agents? The interesting question is no longer just: It may increasingly become: I’m curious how companies are handling this in practice. If AI makes someone 2x or 3x more productive, do you simply raise their targets? Or should some of that productivity gain change compensation, job scope, and how performance itself is measured? submitted by /u/Greg_QU
Originally posted by u/Greg_QU on r/ArtificialInteligence